Most enterprise AI adoption programs begin the same way.
The company buys a set of AI tools. Leadership announces that AI is now a priority. Employees receive training on prompts, copilots, or agents. A few people become enthusiastic. Many use the tools occasionally. Others avoid them. Months later, the organization is still asking how to drive adoption.
I think that approach attacks the problem backwards.
Your people should not have to adopt AI. The workflow should.
That sentence is the simplest explanation of SHIM, the methodology we developed at Very Big Things for transforming work around AI.
The idea is not to hide AI from the organization or pretend it is not involved. The idea is to stop making AI adoption an extra job for every employee.
People should not need to become prompt engineers. They should not have to decide which model to use, remember which tool contains which feature, or carry context from their real work into a separate AI window.
They should get a better way to do the work.
Then the workflow should gradually become more capable behind them.
Most work is digital, but it is not actually connected
A typical business process already uses software.
It may begin in email, move into a spreadsheet, pull information from a CRM, require a document from a shared drive, trigger an approval in chat, and end with someone updating an ERP or ticketing system.
Every individual tool may work exactly as designed. The problem is that the process does not live in any one of them.
The employee becomes the integration layer.
They search for the context. They remember what already happened. They copy information between systems. They chase approvals. They notice the exception. They decide what should happen next. They manually keep the process alive.
This is why giving the employee another AI tool often disappoints.
The tool may draft an email or summarize a document. That can be useful, but the employee still has to leave the process, gather the right information, prompt the AI, review the result, move it back into the work, and update the other systems.
One task became faster. The workflow did not change.
First, give the process one simple interface
SHIM stands for Systems Harmonization and Iterative Modernization.
The first move is Systems Harmonization.
Take one important process performed by a department or team. Understand how it actually works across people, systems, information, decisions, approvals, and exceptions. Then build a simple, purpose-built interface around that process.
That interface connects to the systems already behind the work.
The CRM can remain the CRM. The ERP can remain the ERP. The document repository can remain the document repository. SHIM does not require the company to replace every useful system before it can improve the work.
What changes is the employee experience.
Instead of becoming a swivel chair between systems, the employee can see the process, its current state, the information required, the decisions waiting, and the next actions in one place.
This first release does not need to look like AI at all.
In fact, it usually should not.
The team is adopting a tool it helped shape around a process it already understands. The immediate value is simpler work: fewer screens, fewer searches, fewer handoffs, and clearer ownership.
That alone can be a meaningful improvement.
Then remove manual steps behind the interface
The second move is Iterative Modernization.
Once the process has one coherent place to operate, AI can begin taking work off the employee's plate.
Not all at once.
One step at a time.
The system might first gather information from several sources and prepare the context. A later release might check whether a submission is complete. Another might draft the output. Another might recommend the next action. Another might update the right systems after a person approves the work.
The interface remains familiar.
The amount of work the person has to perform inside it keeps shrinking.
This is a fundamentally different adoption model.
Employees do not experience a sequence of AI rollouts. They experience fewer repetitive steps.
They do not need to be persuaded to return to an AI tool. They already work inside the process.
They do not need to learn the architecture behind the system. They simply notice that the next release saves them another twenty minutes, removes another handoff, or prepares something they used to build manually.
People adopt a better workflow. The workflow gradually adopts AI.
Adoption happens because the work gets better
Most change-management plans treat adoption as a communications and training problem.
Sometimes it is. But often the deeper issue is that the new technology is asking employees to do additional work before it creates enough value for them.
They must learn the tool, remember to use it, figure out what it is good at, supply the context, and recover when it fails.
SHIM changes the bargain.
The team participates in designing a better interface around its real process. The first version already reduces friction. AI improvements then arrive inside that same experience.
Adoption happens because the experience gets better, not because the organization successfully convinced everyone to use AI.
This does not mean AI should be secret.
AI should be unobtrusive to the user, not opaque to the organization.
The company should know where AI is operating, which information it can access, what it is preparing or completing, how quality is evaluated, which actions require approval, and where a person remains accountable.
The employee should be able to understand when the system has prepared work and how to review or correct it.
But they should not have to think about model routing, prompt design, agent orchestration, or token usage to complete their job.
The interface changes the technical problem too
This approach is not only easier for people to adopt. It also creates the conditions AI needs in order to work reliably.
When a process is fragmented, much of its state exists only in people's heads.
The system does not know what has already happened. It does not know which version of the information is authoritative. It may not know which action is allowed, what exception applies, or what success means.
A coherent interface makes those elements explicit.
The workflow has state. The relevant context can be assembled. Rules and decisions can be defined. Approvals and exceptions can be designed. Results can be measured.
The same design that makes the work easier for the employee makes the process more operable by AI.
That is the beauty of the method.
The technical problem and the adoption problem are not solved separately. They are solved through the same workflow design.
Agents are the last chapter
Many companies are beginning their AI strategy with agents.
I think that is usually too early.
An agent placed on top of a fragmented process inherits all of the fragmentation. It still needs context, permissions, actions, workflow state, quality controls, and a way to know when a person should make the decision.
Without those conditions, the agent is not an operating model. It is another actor trying to navigate the maze.
As the workflow matures, the system can take on more responsibility.
People may move from performing every step to reviewing prepared work, making important decisions, and handling the situations where judgment, accountability, creativity, or relationships matter most.
Eventually, an agent may coordinate a larger portion of the process and ask for human input at the right moments.
That can be a powerful destination.
It should not be the first screen.
What this looks like in production
At World Emblem, the first opportunity was a design-production workflow with a backlog the company could not hire its way out of.
The work was broken down task by task. AI was applied where it created real value. Over time, routine production work became approximately 70 percent automated, turnaround moved from weeks to minutes, and the success expanded into additional departments.
At Zumba, the starting point was translation. But translation was not really a language problem. It was a workflow problem spanning agencies, creative tools, reviewers, approvals, code, and distribution.
The first workflow generated more than $500,000 in annual value. Then the capability expanded into a broader AI-enabled marketing operation.
Neither story began with an enterprise-wide demand that everyone adopt AI.
They began with one important process and a better way to run it.
The leadership question to ask
Do not begin by asking:
How do we get more employees to use AI?
Ask:
Which important process is forcing our people to carry information between systems, and what would it look like to give that process one clear place to operate?
Then ask:
Which manual step should the system remove first?
That is a simpler path to AI transformation.
It is also a more ambitious one.
It does not settle for individual productivity. It changes how the work gets done.
